在基于社交媒体的心理健康障碍分类中使用大型语言模型来理解自杀倾向:对Reddit帖子的语言分析
Brian Bauer1, Raquel Norel2, Alex Leow3,4
1Department of Psychology, University of Georgia, Athens, GA, United States.
JMIR mental health
|May 21, 2024
概括
大型语言模型分析了Reddit的帖子,以了解自杀倾向的语言. 这些发现支持了关于自杀和心理健康分类的理论,为在线情绪体验提供了新的见解.
科学领域:
- 计算语言学计算语言学
- 心理健康研究 心理健康研究
- 自然语言处理自然语言处理.
背景情况:
- 自1999年以来,自杀率上升了35%以上.
- 预测和治疗自杀风险仍然是一个挑战.
- 目前对自杀风险因素的理解需要进一步发展.
研究的目的:
- 利用大型语言模型 (LLM) 来分析关于自杀的在线讨论中的语言.
- 在公共网络论坛中探索语言模式,特别是Reddit.
- 为了获得与自杀风险相关的自然语言使用的见解.
主要方法:
- 应用了基于LLM的句子嵌入来从Reddit帖子中提取语言维度.
- 使用缩小维度来总结和可视化复杂的语言数据.
- 从2010年到2022年,分析了30个心理健康子reddit (包括r/SuicideWatch) 的290万条帖子.
主要成果:
- 确定了自杀倾向讨论中的关键主题:脱节,负担,绝望,绝望,辞职和创伤.
- 发现了不同的潜在语言维度:幸福感,寻求支持,以及痛苦的严重程度.
- 发现 subreddit 集群与心理病学等级分类学 (HiTOP) 超光谱保持一致.
结论:
- 为基于语言的自杀理论和维度心理健康分类系统提供了数据驱动的支持.
- 展示了自然语言处理 (NLP) 技术的实用性,以更深入地了解在线情感体验.
- 强调了这种方法在验证或反驳心理健康理论方面的潜力.
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